{
 "metadata": {
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 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "id": "4A62B11436D74388852245B505E65EF3",
     "metadata": {},
     "source": [
      "\u5408\u5e76 \u8ffd\u52a0"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "id": "4A77B7E551A84E65BC04BCC301D69142",
     "input": [
      "df_quotation = DataAPI.MktEqudAdjGet(tradeDate=u\"\",secID=u\"600000.XSHG\",ticker=u\"\",beginDate=u\"20180101\",endDate=u\"20180121\",\n",
      "                                     isOpen=\"\",field=u\"secID,tradeDate,closePrice\",pandas=\"1\")\n",
      "df_factor = DataAPI.MktStockFactorsDateRangeGet(secID=u\"600000.XSHG\",ticker=u\"\",beginDate=u\"20180101\",endDate=u\"20180121\",\n",
      "                                                field=u\"secID,tradeDate,MA10,MA5\",pandas=\"1\")\n",
      "df_quotation = df_quotation.merge(df_factor,on=['tradeDate'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "14D21D1A623344BC8E056737F63EA289",
     "input": [
      "df_quotation[(df_quotation.closePrice > df_quotation.MA10) & (df_quotation.closePrice> df_quotation.MA5)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>secID_x</th>\n",
        "      <th>tradeDate</th>\n",
        "      <th>closePrice</th>\n",
        "      <th>secID_y</th>\n",
        "      <th>MA10</th>\n",
        "      <th>MA5</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-02</td>\n",
        "      <td>12.72</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.650</td>\n",
        "      <td>12.622</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-03</td>\n",
        "      <td>12.66</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.641</td>\n",
        "      <td>12.626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-04</td>\n",
        "      <td>12.66</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.634</td>\n",
        "      <td>12.634</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-05</td>\n",
        "      <td>12.69</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.633</td>\n",
        "      <td>12.664</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-09</td>\n",
        "      <td>12.70</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.650</td>\n",
        "      <td>12.678</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-10</td>\n",
        "      <td>13.02</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.688</td>\n",
        "      <td>12.750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-11</td>\n",
        "      <td>12.94</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.720</td>\n",
        "      <td>12.806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-12</td>\n",
        "      <td>12.91</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.757</td>\n",
        "      <td>12.850</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-15</td>\n",
        "      <td>13.02</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.800</td>\n",
        "      <td>12.918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-17</td>\n",
        "      <td>13.10</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.862</td>\n",
        "      <td>12.974</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-18</td>\n",
        "      <td>13.24</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.920</td>\n",
        "      <td>13.034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>13.24</td>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>12.975</td>\n",
        "      <td>13.100</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "        secID_x   tradeDate  closePrice      secID_y    MA10     MA5\n",
        "0   600000.XSHG  2018-01-02       12.72  600000.XSHG  12.650  12.622\n",
        "1   600000.XSHG  2018-01-03       12.66  600000.XSHG  12.641  12.626\n",
        "2   600000.XSHG  2018-01-04       12.66  600000.XSHG  12.634  12.634\n",
        "3   600000.XSHG  2018-01-05       12.69  600000.XSHG  12.633  12.664\n",
        "5   600000.XSHG  2018-01-09       12.70  600000.XSHG  12.650  12.678\n",
        "6   600000.XSHG  2018-01-10       13.02  600000.XSHG  12.688  12.750\n",
        "7   600000.XSHG  2018-01-11       12.94  600000.XSHG  12.720  12.806\n",
        "8   600000.XSHG  2018-01-12       12.91  600000.XSHG  12.757  12.850\n",
        "9   600000.XSHG  2018-01-15       13.02  600000.XSHG  12.800  12.918\n",
        "11  600000.XSHG  2018-01-17       13.10  600000.XSHG  12.862  12.974\n",
        "12  600000.XSHG  2018-01-18       13.24  600000.XSHG  12.920  13.034\n",
        "13  600000.XSHG  2018-01-19       13.24  600000.XSHG  12.975  13.100"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "29CD9A7C76F54259948E55EAA34CB6DE",
     "metadata": {},
     "source": [
      "\u6570\u636e\u900f\u89c6"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "BA4F77588A2D48B382A9C72D2AA5DC64",
     "input": [
      "df_quotation = DataAPI.MktEqudAdjGet(tradeDate=u\"\",secID=[\"600000.XSHG\",'000001.XSHE'],ticker=u\"\",beginDate=u\"20180118\",endDate=u\"20180121\",\n",
      "                                     isOpen=\"\",field=u\"secID,tradeDate,marketValue\",pandas=\"1\")\n",
      "df_quotation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>secID</th>\n",
        "      <th>tradeDate</th>\n",
        "      <th>marketValue</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>000001.XSHE</td>\n",
        "      <td>2018-01-18</td>\n",
        "      <td>252748455808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>000001.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>254122088720</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-18</td>\n",
        "      <td>388621544496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>600000.XSHG</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>388621544496</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "         secID   tradeDate   marketValue\n",
        "0  000001.XSHE  2018-01-18  252748455808\n",
        "1  000001.XSHE  2018-01-19  254122088720\n",
        "2  600000.XSHG  2018-01-18  388621544496\n",
        "3  600000.XSHG  2018-01-19  388621544496"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "82CEE58C056F4D518A5BF08ADB67AF83",
     "input": [
      "df_quotation = df_quotation.pivot_table(index='tradeDate',columns='secID',values='marketValue')\n",
      "df_quotation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>secID</th>\n",
        "      <th>000001.XSHE</th>\n",
        "      <th>600000.XSHG</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tradeDate</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2018-01-18</th>\n",
        "      <td>252748455808</td>\n",
        "      <td>388621544496</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2018-01-19</th>\n",
        "      <td>254122088720</td>\n",
        "      <td>388621544496</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 57,
       "text": [
        "secID        000001.XSHE   600000.XSHG\n",
        "tradeDate                             \n",
        "2018-01-18  252748455808  388621544496\n",
        "2018-01-19  254122088720  388621544496"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "C0FCC6E2110A4378AE60A2D6694708A3",
     "input": [
      "df_quotation.sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "tradeDate\n",
        "2018-01-18    641370000304\n",
        "2018-01-19    642743633216\n",
        "dtype: int64"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "BA92B0EAF5FE45FC8D03858342916340",
     "metadata": {},
     "source": [
      "\u6570\u636e\u8fc7\u6ee4"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "54487ECD9B294118955A063914EEC6B1",
     "input": [
      "df = DataAPI.MktStockFactorsOneDayGet(tradeDate=u\"20180119\",secID=set_universe('A'),ticker=u\"\",               field=u\"secID,tradeDate,PE\",pandas=\"1\")\n",
      "df[df.PE>0].head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>secID</th>\n",
        "      <th>tradeDate</th>\n",
        "      <th>PE</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>000001.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>11.0330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>000002.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>18.2449</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>000004.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>94.2883</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>000006.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>13.2546</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>000007.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>81.3401</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>000008.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>54.4002</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>000009.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>71.6105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>000010.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>143.7984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>000011.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>11.3323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>000012.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>28.3515</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 61,
       "text": [
        "          secID   tradeDate        PE\n",
        "0   000001.XSHE  2018-01-19   11.0330\n",
        "1   000002.XSHE  2018-01-19   18.2449\n",
        "2   000004.XSHE  2018-01-19   94.2883\n",
        "4   000006.XSHE  2018-01-19   13.2546\n",
        "5   000007.XSHE  2018-01-19   81.3401\n",
        "6   000008.XSHE  2018-01-19   54.4002\n",
        "7   000009.XSHE  2018-01-19   71.6105\n",
        "8   000010.XSHE  2018-01-19  143.7984\n",
        "9   000011.XSHE  2018-01-19   11.3323\n",
        "10  000012.XSHE  2018-01-19   28.3515"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "701C682B023C4FEDA54E1BA7E7FBE04B",
     "metadata": {},
     "source": [
      "\u6570\u636e\u63a2\u7d22\u4e0e\u6570\u636e\u6e05\u6d17"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "29A289954E4B46F8A1DCCDF025B63834",
     "input": [
      "df = DataAPI.MktStockFactorsOneDayGet(tradeDate=u\"20180119\",secID=set_universe(\"A\"),ticker=u\"\",\n",
      "                                      field=u\"secID,tradeDate,ROE\",pandas=\"1\")\n",
      "df.plot.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xbb87a90>"
       ]
      },
      {
       "data": {
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        "text/plain": "<matplotlib.figure.Figure at 0xb363d50>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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fa0jC6CBXBeB6CH8AN2VAZKzCB8dLkk63eYJcDQB/EP4Abgvenp4rdhsMGzZc\nYWFhQaoIsC/CH8Bt4Ux7o9b+rEkDIk9Ikk63ndT6F6cpKWlEkCsD7IfwB3DbuHQXAgDrcKofAACG\nIfwBADAM4Q8AgGEIfwAADEP4AwBgGMIfAADDEP4AABiG8AcAwDCEPwAAhiH8AQAwDOEPAIBhCH8A\nAAzDjX0A3JDu7m7V1R2VpCtuwRtIX77FL7f3BQKH8AdwQ+rqjmrB67s0IDJWp479XkMSRlsyz6W3\n+OX2vkBgEf4AbtjFW++ebvPcknkABBb7/AEAMAzhDwCAYQh/AAAME1L7/Pfu3avVq1fL6/Vq5syZ\nmjdvXrBLAmzv0qP7JWuP8P8qXz7yX+Lof+BmhEz49/T06NVXX9WmTZsUGxurRx99VOnp6UpKSgp2\naYCtXXp0vyRLj/D/Kpce+S9Jna0NWvLEg/ra1xJ9y/BjAPBfyIR/bW2tEhMTFR9/4cjf7OxsVVdX\nE/5AL3x5bb67u1uSQ2FhfS57LF1Y07/0qHurj/D/Kl+uYe3PPvL9GOBUQODGhEz4ezwexcXF+Z67\nXC4dPHgwiBUBV/flYJUuXyvt7u7WJ598oubmjqu+fq3P+nIwf/lzbyTQL4Tn/1+bvzNiiO/c/YuP\nL752q9f0/XHpj4FLdwt8uVfp2t8TWwxgopAJ/1DV2dmpF5YuVU93tyTpa1/7mp56/LGAfHZLS/hl\nAWInodxbff1n+l7JL3VHeJQk6WxHs16e+xe+TdTXe/1an9XmOar+A+/SHeFR1/3cS5e99PHF1+6K\nG+l3T6fbTvoen2lvluS44vGNPg/kss1/+L/6Xsnvrtrrtb6na3331xLK/z79Yef+etObHbcoObxe\nrzfYRfjjv/7rv/TGG29ow4YNkqTi4mJJ4qA/AABuUMic6jdmzBjV19fr+PHjOnfunMrKypSenh7s\nsgAACDkhs9k/LCxM3/3ud5Wfny+v16tHH32Ug/0AAOiFkNnsDwAAAiNkNvsDAIDAIPwBADAM4Q8A\ngGGMCP+NGzdq1KhRam1tDXYpAbV+/XpNmzZNubm5mj17thoaGoJdUkCtWbNGWVlZys3NVWFhoTo6\n7HXe8dtvv62pU6dq9OjROnToULDLCYi9e/fqW9/6ljIzM32n49pJUVGRJk6cqJycnGCXEnANDQ36\n9re/rezsbOXk5Gjz5s3BLimgzp07p8cee0zTp09Xdna2fvjDHwa7pIDr6elRXl6enn/++esua/vw\nb2ho0P7VJHJbAAAEFElEQVT9+3X33XcHu5SAe+6557Rr1y7t3LlT6enpeuONN4JdUkA9/PDDKisr\n086dO5WYmCi32x3skgJq5MiR+od/+Af9yZ/8SbBLCYiL99/YsGGDfvGLX6isrExHjhwJdlkBNWPG\nDN+1RuwmLCxMy5YtU1lZmX76059qy5Yttvr79evXT5s3b9aOHTu0a9cuvf/++/rggw+CXVZAbd68\n2e+z4Gwf/qtXr9bSpUuDXYYlBg4c6Ht85swZDR48OIjVBN7EiRPVp8+Ff6IPPPCA7bZsDB8+XMOG\nDZNdTri59P4bffv29d1/w07Gjh2rQYMGBbsMS8TExGj06AuXcR44cKCSkpJ08uTJ67wrtNx5552S\nLmwF6OnpUWRkZJArCpyGhgbt2bNHjz3m3xVkQ+Y8/96orq5WXFyc7r333mCXYpkf/ehH2rlzp+64\n4w79/Oc/D3Y5ltm6dauys7ODXQaugftv2MexY8f08ccfKyUlJdilBFRPT49mzJih+vp6PfHEE0pO\nTg52SQFzcUW3vb3dr+VDPvyfffZZNTU1XTG+cOFCud1ubdy40TcWimtYX9XfokWLlJaWpkWLFmnR\nokUqLi7W6tWr9dprrwWhyt67Xn+S9Oabb6pv374huZ/Vn/6A20lnZ6fmz5+voqKiy7Yu2kGfPn20\nY8cOdXR0KD8/X7/61a80bty4YJd10959911FR0dr9OjROnDggF/vCfnw/8lPfnLV8U8++UTHjx9X\nbm6uvF6vPB6PZs6cqZ///OcaMmTILa6y976qvy/LyckJyfscXK+/7du3a8+ePSF78JG/fz87cLlc\n+sMf/uB77vF4FBsbG8SKcKPOnz+v+fPnKzc3V5MnTw52OZYJDw/XI488ot/+9re2CP8PP/xQNTU1\n2rNnj7q6utTZ2amlS5dqzZo1X/ke2+7zHzlypPbv36/q6mrV1NTI5XKptLQ0pIL/ej777DPf46qq\nKo0aNSqI1QTe3r17tWHDBr355pvq169fsMuxVChulfoyU+6/YYe/1VcpKipScnKynnnmmWCXEnDN\nzc2+TeJnz57Vv//7v/uOcQh1L7zwgt59911VV1frhz/8ocaPH3/N4JdssObvL4fDYbv/adeuXatP\nP/1UYWFhuueee7RixYpglxRQ3/ve9/TFF18oPz9fknT//ffbqseqqiq9+uqramlp0fPPP69Ro0bp\nxz/+cbDL6jUT7r+xePFiHThwQK2trZo0aZIKCws1c+bMYJcVEB988IF2796tkSNHavr06XI4HFq0\naJFSU1ODXVpANDY26qWXXpLX61VPT49yc3M1YcKEYJcVNFzbHwAAw9h2sz8AALg6wh8AAMMQ/gAA\nGIbwBwDAMIQ/AACGIfwBADAM4Q8AgGEIfwAADPP/AM5AXQ/HhPFeAAAAAElFTkSuQmCC\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0xb363d50>"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "F12170D8B886428E87FB7B4EA026B82A",
     "input": [
      "_ = df.boxplot(sym='rs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "data": {
        "image/png": 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        "text/plain": "<matplotlib.figure.Figure at 0xb6b9350>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZZ5QxHAC0hNK+R1ypVByaBoBTVNqKGAA4de6sBQCJhBgAEgkxACQSYgBIdMIbegATW29v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\nkEiIASCREANAov8HVTu4ysycfsQAAAAASUVORK5CYII=\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0xb6b9350>"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "97AA60DECFA1493B8A01AEA441EE4F8F",
     "input": [
      "df_ = df.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "16FFEEADA19A4374810A97534881923A",
     "input": [
      "df_.loc[df.ROE-df.ROE.mean()<-3*df.ROE.std(),'ROE'] = df.ROE.mean()-3*df.ROE.std()\n",
      "df_.loc[df.ROE-df.ROE.mean()>3*df.ROE.std(),'ROE'] = df.ROE.mean()+3*df.ROE.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "6CBF9C173AA542C6B1904483FBCEDC61",
     "input": [
      "df_.plot.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0xbc2a150>"
       ]
      },
      {
       "data": {
        "image/png": 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        "text/plain": "<matplotlib.figure.Figure at 0xbc359d0>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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Edj+g9/U33xns0vnDWD19AOj+c7i8MHKEXeh7PB6NHPn/x6nS09P13nvvhbCi\n8GK1t+uzT2t0/Pjfu3wi70lPRwwDXTY0kJ3T7nb31b2e/lzz3tM+uGYeJuppWeCehrR6+5u3u3S0\nnfcbu4sNBX4vEGcNnBV2oY/zxV4co/aTHR98Wus/UVmlW5e8WqkmT6UuHznOv11bU12Xf/BtTXWS\npNPNXkkuSZL3s7/qx09/oEsSkiVJTZ5KxcZfrksSkvVFi1ffX/JNXXXVqC6vD9xv575//PRruiQh\nucvre9tXd4H77qmezp4C6+7t6+77sPN6u/sO59eEWz3h/JpwqycYr2lrqtNHH30kr7el139P3d8f\ngllPT+8JPdXQ/T2l831DUq/vEYHbdd+m+/sS+s9lWZYV6iICvfPOO3rqqaf0i1/8QpJUXl4uSUzm\nAwBgkIaFuoDuJkyYoOrqan366ac6e/asXnnlFU2bNi3UZQEAEPHC7vS+2+3Www8/rMWLF8uyLN1+\n++1M4gMAIAjC7vQ+AABwRtid3gcAAM4g9AEAMAShDwCAISIu9JuamrR48WLNmDFD9957r5qbm3vc\nrrm5WSUlJcrJyVFubq7efffdIa7UGXb7lzqWNC4oKNB99903hBU6y07/tbW1WrhwoXJzczV79mw9\n99xzIag0eA4ePKjbbrtNM2bM8F/C2t2Pf/xjZWdnKz8/X0ePHh3iCp11of537dqlvLw85eXlad68\nefrrX/8agiqdY+f3L3WsZnrdddfp1VdfHcLqnGen/8OHD2vOnDmaNWuW7r777iGu0DkX6r2xsVHf\n/va3lZ+fr9mzZ2v79u0X3qkVYZ544gmrvLzcsizLKisrs9avX9/jdg8++KC1detWy7Is68svv7Sa\nm5uHrEYn2e3fsizr2WeftVauXGkVFxcPVXmOs9N/XV2d9cEHH1iWZVktLS1Wdna29eGHHw5pncHi\n8/ms6dOnWzU1NdbZs2etvLy883p5/fXXrSVLlliWZVnvvPOOdccdd4SiVEfY6f8vf/mL9fnnn1uW\nZVkHDhwwrv/O7RYuXGgtXbrU2rt3bwgqdYad/j///HNr5syZVm1trWVZlnXy5MlQlBp0dnp/6qmn\nrCeffNKyrI6+J0+ebH355Zd97jfijvQrKipUUFAgSSooKNC+ffvO26alpUVvvvmmCgsLJUkxMTFK\nSEgY0jqdYqd/qeNo98CBA7rjjjuGsjzH2ek/NTVV48d3rB0eHx+vrKws1dVF5updgfeiuOiii/z3\noghUUVGmQc9MAAAEoUlEQVShOXPmSJKuv/56NTc3q6GhIRTlBp2d/m+44QYlJib6v/Z4omdZZTv9\nS9KWLVs0Y8YMJScnh6BK59jpf9euXcrOzlZ6erokRc3/Azu9p6SkqLW1VZLU2tqqyy+/XDExfV+J\nH3Gh7/V6lZKSIqnjzd3r9Z63TU1NjYYPH641a9aooKBADz/8sL744ouhLtURdvqXpMcee0yrV6+W\ny+Xq8fuRym7/nWpqanTs2DFNnDhxKMoLup7uRdH9A0xdXZ2uuOKKLttES/DZ6T/QSy+9pClTpgxF\naUPCTv8ej0f79u3T/Pnzh7o8x9npv6qqSk1NTbr77rtVWFio3/72t0NdpiPs9H7nnXfq73//u269\n9Vbl5+ertLT0gvsNu8V5JKmoqKjHI5X777//vOd6CrVz587pgw8+0A9+8ANNmDBBP/nJT1ReXq6S\nkhJH6g22wfb/+uuvKyUlRePHj9fhw4cdqdFJg+2/U2trq0pKSlRaWqr4+Pig1ojw86c//Unbt2/X\n//zP/4S6lCH12GOPadWqVf7HlmFLr/h8Pn3wwQf61a9+pba2Nt1111268cYbNWrU+ff9iDZlZWW6\n9tprtWXLFlVXV6uoqEg7d+7s8/0uLEP/2Wef7fV7I0aMUENDg1JSUlRfX9/jqZwrrrhCV1xxhSZM\nmCBJmjFjhp555hnH6g22wfb/9ttva//+/Tpw4IDOnDmj1tZWrV69Wk888YSTZQfNYPuXOj74lZSU\nKD8/X9OnT3eqVMelp6frs88+8z/2eDxKS0vrsk1aWppqa2v9j2tra/2nOiOdnf4l6dixY/rBD36g\nZ555RklJSUNZoqPs9P/+++9rxYoVsixLjY2NOnjwoGJiYqJi+XI7/aenp2v48OGKjY1VbGysJk2a\npGPHjkV86Nvp/e233/ZP1L7qqquUmZmpyspKf/b1JOJO70+dOtU/Q3HHjh09/mGnpKRo5MiR+uij\njyR1HAFEy1K+dvr/3ve+p9dff10VFRX693//d918880RE/gXYqd/SSotLdWYMWO0aNGioSwv6Ozc\ni2LatGn+U5rvvPOOLrvsMv8QSKSz0/9nn32mkpISPfHEE7rqqqtCVKkz7PRfUVGhiooK7d+/X7fd\ndpvWrVsXFYEv2f/7f+utt+Tz+XT69GkdOXIkKt7v7fSelZWlN954Q5LU0NCgqqoqXXnllX3v2KGJ\nh45pbGy0Fi1aZGVnZ1tFRUVWU1OTZVmW5fF4rKVLl/q3O3r0qDV37lwrLy/P+s53vuOf3Rvp7Pbf\n6fDhw1E1e99O/2+++aZ17bXXWnl5eVZ+fr41Z84c68CBA6Ese1AOHDhgZWdnW9/85jetsrIyy7Is\n64UXXrB+85vf+Ld55JFHrOnTp1uzZ8+23n///VCV6ogL9b927Vpr8uTJ1pw5c6z8/HyrsLAwlOUG\nnZ3ff6eHHnooqmbvW5a9/p955hlr5syZ1qxZs6znnnsuVKUG3YV6P3nypFVcXGzNnj3bmjVrlrVr\n164L7pO19wEAMETEnd4HAAADQ+gDAGAIQh8AAEMQ+gAAGILQBwDAEIQ+AACGIPQBADAEoQ8AgCH+\nL4js2EHccmQbAAAAAElFTkSuQmCC\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0xbc359d0>"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "00818369C2C7489D8E72D46977A52772",
     "metadata": {},
     "source": [
      "\u6570\u636e\u8f6c\u5316"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "44BE3D9CEEC84AC983C65604858E2838",
     "input": [
      "((df_['ROE'] - df_['ROE'].min()) / (df_['ROE'].max() - df_['ROE'].min())).plot.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x9ae32d0>"
       ]
      },
      {
       "data": {
        "image/png": 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        "text/plain": "<matplotlib.figure.Figure at 0x98dd410>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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K6/W6UBEQ32yF/uzZs3XkyBHdcMMNsixLGzZsUGpqqkpKSpyuD0Ac62xr0qqXmpWYejCw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\nGILQBwDAEIQ+AACG+H/yYg4HnhH2rwAAAABJRU5ErkJggg==\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0x98dd410>"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "D46624FEEA1D4D3881C21CDAAB7B635F",
     "input": [
      "((df_['ROE'] - df_['ROE'].mean()) / df_['ROE'].std()).plot.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x8f15e10>"
       ]
      },
      {
       "data": {
        "image/png": 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        "text/plain": "<matplotlib.figure.Figure at 0xbb83250>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0xbb83250>"
       ]
      }
     ]
    },
    {
     "cell_type": "markdown",
     "id": "30C87A1B678744EDA021CF60241CB6E4",
     "metadata": {},
     "source": [
      "\u54d1\u53d8\u91cf"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "909B4349E5444E1DAD1CF851ED008D8A",
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "3D26EB6295904A2C939F26EA49DF8288",
     "input": [
      "df_industry = DataAPI.EquIndustryGet(industryVersionCD=u\"010303\",industry=u\"\",\n",
      "                                     secID=df_['secID'].tolist(),ticker=u\"\",intoDate=u\"20180101\",field=u\"secID,industryName1\",pandas=\"1\")\n",
      "industry_list = df_industry['industryName1'].drop_duplicates().tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "8FFB2498AD014B768F2E57519E480BCA",
     "input": [
      "def get(x):\n",
      "    ind_s = pd.Series([0]*len(industry_list),index = industry_list)\n",
      "    if len(df_industry[df_industry['secID']==x]) > 0:\n",
      "        ind = df_industry[df_industry['secID']==x]['industryName1'].values[0]\n",
      "        ind_s.loc[ind] = 1\n",
      "    # print ind_s\n",
      "    return ind_s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "2050FD3DE2B24425845B6E7919D6470D",
     "input": [
      "df_[industry_list] = df_['secID'].apply(lambda x: get(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "364C202A79D0493F8539B2AC87DA8202",
     "input": [
      "df_.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>secID</th>\n",
        "      <th>tradeDate</th>\n",
        "      <th>ROE</th>\n",
        "      <th>\u94f6\u884c</th>\n",
        "      <th>\u623f\u5730\u4ea7</th>\n",
        "      <th>\u533b\u836f\u751f\u7269</th>\n",
        "      <th>\u516c\u7528\u4e8b\u4e1a</th>\n",
        "      <th>\u7efc\u5408</th>\n",
        "      <th>\u673a\u68b0\u8bbe\u5907</th>\n",
        "      <th>\u5efa\u7b51\u88c5\u9970</th>\n",
        "      <th>...</th>\n",
        "      <th>\u5546\u4e1a\u8d38\u6613</th>\n",
        "      <th>\u5316\u5de5</th>\n",
        "      <th>\u6709\u8272\u91d1\u5c5e</th>\n",
        "      <th>\u975e\u94f6\u91d1\u878d</th>\n",
        "      <th>\u7535\u6c14\u8bbe\u5907</th>\n",
        "      <th>\u4f11\u95f2\u670d\u52a1</th>\n",
        "      <th>\u56fd\u9632\u519b\u5de5</th>\n",
        "      <th>\u91c7\u6398</th>\n",
        "      <th>\u7eba\u7ec7\u670d\u88c5</th>\n",
        "      <th>\u94a2\u94c1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>000001.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>0.1124</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>000002.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>0.2029</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>000004.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>0.0631</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
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        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>000005.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>-0.0267</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
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        "      <td>1</td>\n",
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        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>000006.XSHE</td>\n",
        "      <td>2018-01-19</td>\n",
        "      <td>0.2078</td>\n",
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        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 31 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "         secID   tradeDate     ROE  \u94f6\u884c  \u623f\u5730\u4ea7  \u533b\u836f\u751f\u7269  \u516c\u7528\u4e8b\u4e1a  \u7efc\u5408  \u673a\u68b0\u8bbe\u5907  \u5efa\u7b51\u88c5\u9970 ...  \\\n",
        "0  000001.XSHE  2018-01-19  0.1124   1    0     0     0   0     0     0 ...   \n",
        "1  000002.XSHE  2018-01-19  0.2029   0    1     0     0   0     0     0 ...   \n",
        "2  000004.XSHE  2018-01-19  0.0631   0    0     1     0   0     0     0 ...   \n",
        "3  000005.XSHE  2018-01-19 -0.0267   0    0     0     1   0     0     0 ...   \n",
        "4  000006.XSHE  2018-01-19  0.2078   0    1     0     0   0     0     0 ...   \n",
        "\n",
        "   \u5546\u4e1a\u8d38\u6613  \u5316\u5de5  \u6709\u8272\u91d1\u5c5e  \u975e\u94f6\u91d1\u878d  \u7535\u6c14\u8bbe\u5907  \u4f11\u95f2\u670d\u52a1  \u56fd\u9632\u519b\u5de5  \u91c7\u6398  \u7eba\u7ec7\u670d\u88c5  \u94a2\u94c1  \n",
        "0     0   0     0     0     0     0     0   0     0   0  \n",
        "1     0   0     0     0     0     0     0   0     0   0  \n",
        "2     0   0     0     0     0     0     0   0     0   0  \n",
        "3     0   0     0     0     0     0     0   0     0   0  \n",
        "4     0   0     0     0     0     0     0   0     0   0  \n",
        "\n",
        "[5 rows x 31 columns]"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "F641E9B1618D452D84516A1B3F906D0E",
     "input": [
      "len(df_['secID'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "3478"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "17CF3585665743589B0280AAAB2180E6",
     "input": [
      "_ = df.boxplot(sym='rs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "data": {
        "image/png": 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ZZ5QxHAC0hNK+R1ypVByaBoBTVNqKGAA4de6sBQCJhBgAEgkxACQSYgBIdMIbegATW29v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\nkEiIASCREANAov8HVTu4ysycfsQAAAAASUVORK5CYII=\n",
        "text/plain": "<matplotlib.figure.Figure at 0xb363e90>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZZ5QxHAC0hNK+R1ypVByaBoBTVNqKGAA4de6sBQCJhBgAEgkxACQSYgBIdMIbegATW29v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\nkEiIASCREANAov8HVTu4ysycfsQAAAAASUVORK5CYII=\n",
       "source": "display",
       "text": [
        "<matplotlib.figure.Figure at 0xb363e90>"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "C9A370F51F944E9C83060C14556EF7C5",
     "input": [
      "df.ROE.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "count    3454.000000\n",
        "mean        0.091654\n",
        "std         0.170462\n",
        "min        -3.384800\n",
        "25%              NaN\n",
        "50%              NaN\n",
        "75%              NaN\n",
        "max         3.225700\n",
        "Name: ROE, dtype: float64"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "8BCE0E5A0CD1489E87610C399ACDED9D",
     "input": [
      "df_ = df.copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "id": "23CDA877EA1B48A09B2B361F65C6EF05",
     "input": [
      "df_3sigma = df[((df.ROE-df.ROE.mean()).abs()>3*df.ROE.std())]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "AAFA95841D894B8B9462D31AE97E11F8",
     "input": [
      "df_[(df.ROE-df.ROE.mean()>3*df.ROE.std())]['ROE'] = df.ROE.mean()+3*df.ROE.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "EEF21535D71D42FD833E936129A6F0CB",
     "input": [
      "df_[(df.ROE-df.ROE.mean()<-3*df.ROE.std())]['ROE'] = df.ROE.mean()-3*df.ROE.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "id": "B94F5FBFFEB847688F64382BA1D59FD8",
     "input": [
      "df_.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "array([[<matplotlib.axes._subplots.AxesSubplot object at 0xb895dd0>]], dtype=object)"
       ]
      },
      {
       "data": {
        "image/png": 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Os0mh/3ES9in0gQMH6qOPPtKZM2cUCAS0Y8cOpaenKysrS2vXrpUkVVZWKjs7W5KUlZWl\nqqoqnT17Vl988YUaGxuVkZER7uEBAIhpYd8DHzJkiPLz81VYWKhu3brpe9/7nn70ox+ptbVVs2bN\n0po1a5Samqrly5dLktLT0+XxeJSbmyun06nS0tKInF4HACAWhR1wSXrsscf02GOPXbKtT58+WrVq\n1RX3Ly4uVnFxcVcOCQAAxDuxAQBgJAIOAICBCDgAAAYi4AAAGIiAAwBgIAIOAICBCDgAAAYi4AAA\nGIiAAwBgIAIOAICBCDgAAAYi4AAAGIiAAwBgIAIOAICBCDgAAAYi4AAAGIiAAwBgIAIOAICBnNFe\nAACzBNrb1dj4efDy7bcPVFxcXBRXBMQmAg7gqpxq8WvZH46qR+8j+qr5S7369IMaNOiOaC8LiDkE\nHMBV69G7n+L7pkZ7GUBM4zFwAAAMRMABADAQAQcAwEAEHAAAAxFwAAAMRMABADAQAQcAwEAEHAAA\nAxFwAAAMRMABADAQAQcAwEAEHAAAAxFwAAAMRMABADBQlwLe0tKimTNnyuPxKDc3Vx999JGam5tV\nVFSknJwcTZs2TS0tLcH9vV6vJkyYII/Ho23btnV58QAAxKouBXzx4sW67777VF1drfXr12vgwIEq\nKyvTmDFjVFNTo9GjR8vr9UqS9u/fr+rqalVVVam8vFyLFi1SIBCIyBAAAMSasAN+8uRJ/f3vf9fk\nyZMlSU6nUwkJCaqrq1NBQYEkqaCgQLW1tZKk+vp6TZw4UU6nUwMGDFBaWpoaGhoiMAIAALEn7IAf\nOnRIffv21fz581VQUKDnnntOp06d0rFjx5ScnCxJcrlcampqkiRZlqWUlJTg7d1utyzL6uLyAQCI\nTc5wb3ju3Dn985//1IIFCzRs2DAtWbJEZWVlcjgcl+x3+eVwuFwJXf4eNzI7z2fn2SR7znf8ePxV\n7Z+YGG/sz8HUdYfKzvPZebZQhR3w/v37q3///ho2bJgkacKECSovL1dSUpKOHj2q5ORk+f1+JSYm\nSjp/j/vIkSPB2/t8Prnd7pCO5fe3dL6ToVyuBNvOZ+fZJPvO19R08qr3N/HnYNff3wV2ns/Os0mh\n/3ES9in05ORkpaSk6LPPPpMk7dixQ+np6crKytLatWslSZWVlcrOzpYkZWVlqaqqSmfPntUXX3yh\nxsZGZWRkhHt4AABiWtj3wCXp17/+tebOnatz587ptttu0wsvvKC2tjbNmjVLa9asUWpqqpYvXy5J\nSk9PD77czOl0qrS0NCKn1wEAiEVdCviQIUO0Zs2aDttXrVp1xf2Li4tVXFzclUMCAADxTmwAABiJ\ngAMAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCAgQg4AAAG\nIuAAABiIgAMAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCA\ngQg4AAAGIuAAABiIgAMAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCAgQg4AAAGIuAAABiIgAMA\nYKAuB7y9vV0FBQV6/PHHJUnNzc0qKipSTk6Opk2bppaWluC+Xq9XEyZMkMfj0bZt27p6aAAAYlaX\nA7569WoNGjQoeLmsrExjxoxRTU2NRo8eLa/XK0nav3+/qqurVVVVpfLyci1atEiBQKCrhwcAICZ1\nKeA+n09btmzRww8/HNxWV1engoICSVJBQYFqa2slSfX19Zo4caKcTqcGDBigtLQ0NTQ0dOXwAADE\nrC4FfMmSJZo3b54cDkdw27Fjx5ScnCxJcrlcampqkiRZlqWUlJTgfm63W5ZldeXwAADErLADvnnz\nZiUnJ2vo0KHfeir84rgDAIDIcIZ7w127dqm+vl5btmzRmTNn1NraqqefflrJyck6evSokpOT5ff7\nlZiYKOn8Pe4jR44Eb+/z+eR2u0M6lsuVEO4yjWDn+ew8m2TP+Y4fj7+q/RMT4439OZi67lDZeT47\nzxaqsAP+1FNP6amnnpIkffDBB1q5cqVefPFFLV26VGvXrtX06dNVWVmp7OxsSVJWVpbmzp2rqVOn\nyrIsNTY2KiMjI6Rj+f0tne9kKJcrwbbz2Xk2yb7zNTWdvOr9Tfw52PX3d4Gd57PzbFLof5yEHfBv\nMn36dM2aNUtr1qxRamqqli9fLklKT0+Xx+NRbm6unE6nSktLOb0OAECYIhLwUaNGadSoUZKkPn36\naNWqVVfcr7i4WMXFxZE4JAAAMY13YgMAwEAEHAAAAxFwAAAMRMABADAQAQcAwEAEHAAAAxFwAAAM\nRMABADAQAQcAwEAEHAAAAxFwAAAMRMABADAQAQcAwEAEHAAAAxFwAAAMRMABADAQAQcAwEAEHAAA\nAxFwAAAMRMABADAQAQcAwEAEHAAAAzmjvQAA5gq0t6ux8fNLtt1++0DFxcVFaUVA7CDgAMJ2qsWv\nZX84qh69j0iSvmr+Uq8+/aAGDbojyisD7I+AA+iSHr37Kb5varSXAcQcHgMHAMBABBwAAAMRcAAA\nDETAAQAwEAEHAMBABBwAAAMRcAAADETAAQAwEAEHAMBABBwAAAOFHXCfz6dHH31Uubm5ysvL0+rV\nqyVJzc3NKioqUk5OjqZNm6aWlpbgbbxeryZMmCCPx6Nt27Z1ffUAAMSosAMeFxen+fPna9OmTXr7\n7bf11ltv6cCBAyorK9OYMWNUU1Oj0aNHy+v1SpL279+v6upqVVVVqby8XIsWLVIgEIjYIAAAxJKw\nA+5yuTR06FBJUs+ePTVo0CBZlqW6ujoVFBRIkgoKClRbWytJqq+v18SJE+V0OjVgwAClpaWpoaEh\nAiMAABB7IvIY+KFDh/TJJ59o+PDhOnbsmJKTkyWdj3xTU5MkybIspaSkBG/jdrtlWVYkDg8AQMzp\ncsBbW1s1c+ZMlZSUqGfPnnI4HJdcf/llAADQdV36PPBz585p5syZys/P1/333y9JSkpK0tGjR5Wc\nnCy/36/ExERJ5+9xHzlyJHhbn88nt9sd0nFcroSuLPOGZ+f57DybZM/5jh+P79LtExPjjfm5mLLO\ncNl5PjvPFqouBbykpETp6emaMmVKcFtWVpbWrl2r6dOnq7KyUtnZ2cHtc+fO1dSpU2VZlhobG5WR\nkRHScfz+ls53MpTLlWDb+ew8m2Tf+ZqaTnb59ib8XOz6+7vAzvPZeTYp9D9Owg74hx9+qI0bN2rw\n4MGaNGmSHA6HZs+erV/84heaNWuW1qxZo9TUVC1fvlySlJ6eLo/Ho9zcXDmdTpWWlnJ6HQCAMIUd\n8HvuuUf/+te/rnjdqlWrrri9uLhYxcXF4R4SAAD8f7wTGwAABiLgAAAYiIADAGAgAg4AgIEIOAAA\nBiLgAAAYiIADAGAgAg4AgIEIOAAABiLgAAAYiIADAGCgLn0aGQDztbW16eDBT4OXGxs/j+JqAISK\ngAMx7uDBT/XkixvUo3c/SdKxQ/9S0oChUV4VgM4QcADq0buf4vumSpK+araivBoAoSDgACIm0N7e\n4RT87bcPVFxcXJRWBNgXAQcQMada/Fr2h6Pq0fuIJOmr5i/16tMPatCgO6K8MsB+CDiAiLr4dDyA\na4eXkQEAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCAgQg4AAAGIuAAABiIgAMAYCACDgCAgQg4\nAAAGIuAAABiIDzMBYlBbW5sOHvxUkjp8/GckXf7xony0KBA5BByIQQcPfqonX9ygHr376dihfylp\nwNBrcpyLP16UjxYFIouAAzHqwsd+ftVsXZfjAIgsHgMHAMBABBwAAAMRcAAADHTdHwPfunWrlixZ\nokAgoMmTJ2v69OnXewlAzLn4WefStX3m+Te5/BnpEs9KB7riuga8vb1dzz//vFatWqV+/frpoYce\nUnZ2tgYNGnQ9lwHEnIufdS7pmj7z/Jtc/Ix0SWo94dPcH39f3/lOWnAfgg6E7roGvKGhQWlpaUpN\nPf+M1NzcXNXV1RFwIAyX36tua2uT5FBcXLdLvpbO3+O++Nng1/qZ59/k8jUs+8NHwaDzMjPg6lzX\ngFuWpZSUlOBlt9utPXv2XM8lACG5PI7SpfcO29ratG/fPjU1nbzi9d/2vS6P6+Xf92qifD6A/3Ov\n+paEpOBruy98feG6632POxQXB/3iU+yXzyp9+8+Je+6IRbwOPAStra16at48tbe1SZK+853v6P/8\n74cj8r2PH4+/JAJ2YvJsjY2f6zflf9bN8YmSpNMnm/TrX/wgeLq3s+u/7Xs1W5/qf/Xso5vjEzv9\nvhfve/HXF67rkzI45Jm+av4y+PWpliZJjg5fX+3lSO7b9J9/6zfl/7zirN/2c/q2n/23MfnfZyjs\nPF84s9nxzI4jEAgErtfB/vGPf+i1115TRUWFJKmsrEySeCIbAABX6bq+jGzYsGFqbGzU4cOHdfbs\nWW3atEnZ2dnXcwkAANjCdT2FHhcXp+eee05FRUUKBAJ66KGHeAIbAABhuK6n0AEAQGTwTmwAABiI\ngAMAYCACDgCAgYwJ+MqVKzVkyBCdOHEi2kuJqFdffVUPPvig8vPzNXXqVPl8vmgvKaKWLl0qj8ej\n/Px8zZgxQydP2ut1qe+9954eeOABDR06VHv37o32ciJi69at+uEPf6icnJzgSz3tpKSkRGPHjlVe\nXl60lxJxPp9Pjz76qHJzc5WXl6fVq1dHe0kRdfbsWT388MOaNGmScnNz9fLLL0d7SRHX3t6ugoIC\nPf74453ua0TAfT6ftm/frltvvTXaS4m4xx57TBs2bND69euVnZ2t1157LdpLiqhx48Zp06ZNWr9+\nvdLS0uT1eqO9pIg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        "text/plain": "<matplotlib.figure.Figure at 0xa421c90>"
       },
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "source": "display",
       "text": [
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    {
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     "language": "python",
     "metadata": {},
     "outputs": []
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    {
     "cell_type": "code",
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     "input": [
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     "metadata": {},
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}